Search Results for author:
Found 23 papers, 9 papers with code
UniHuman: A Unified Model for Editing Human Images in the Wild
In this paper, we propose UniHuman, a unified model that addresses multiple facets of human image editing in real-world settings.
Fine-grained Text and Image Guided Point Cloud Completion with CLIP Model
In this work, we propose a novel multimodal fusion network for point cloud completion, which can simultaneously fuse visual and textual information to predict the semantic and geometric characteristics of incomplete shapes effectively.
Leaf Cultivar Identification via Prototype-enhanced Learning
Plant leaf identification is crucial for biodiversity protection and conservation and has gradually attracted the attention of academia in recent years.
Collecting The Puzzle Pieces: Disentangled Self-Driven Human Pose Transfer by Permuting Textures
Then we reconstruct the input image by sampling from the permuted textures for patch-level disentanglement.
Supervised Attribute Information Removal and Reconstruction for Image Manipulation
Thus, the source attribute information can often be hidden in the disentangled features, leading to unwanted image editing effects.
A Unified Weight Initialization Paradigm for Tensorial Convolutional Neural Networks
To address this problem, we propose a universal weight initialization paradigm, which generalizes Xavier and Kaiming methods and can be widely applicable to arbitrary TCNNs.
BViT: Broad Attention based Vision Transformer
In this paper, we propose the broad attention to improve the performance by incorporating the attention relationship of different layers for vision transformer, which is called BViT.
Stacked BNAS: Rethinking Broad Convolutional Neural Network for Neural Architecture Search
Moreover, multi-scale feature fusion and knowledge embedding are proposed to improve the performance of BCNN with shallow topology.
ABCP: Automatic Block-wise and Channel-wise Network Pruning via Joint Search
Compared with the traditional rule-based pruning method, this pipeline saves human labor and achieves a higher compression ratio with lower accuracy loss.
Predicate correlation learning for scene graph generation
For a typical Scene Graph Generation (SGG) method, there is often a large gap in the performance of the predicates' head classes and tail classes.
Heuristic Rank Selection with Progressively Searching Tensor Ring Network
Interestingly, we discover that part of the rank elements is sensitive and usually aggregate in a narrow region, namely an interest region.
BNAS-v2: Memory-efficient and Performance-collapse-prevented Broad Neural Architecture Search
For this consequent issue, two solutions are given: 1) we propose Confident Learning Rate (CLR) that considers the confidence of gradient for architecture weights update, increasing with the training time of over-parameterized BCNN; 2) we introduce the combination of partial channel connections and edge normalization that also can improve the memory efficiency further.
Towards Visual Distortion in Black-Box Attacks
Constructing adversarial examples in a black-box threat model injures the original images by introducing visual distortion.
Learning Compact Reward for Image Captioning
Adversarial learning has shown its advances in generating natural and diverse descriptions in image captioning.
BNAS:An Efficient Neural Architecture Search Approach Using Broad Scalable Architecture
In this paper, we propose Broad Neural Architecture Search (BNAS) where we elaborately design broad scalable architecture dubbed Broad Convolutional Neural Network (BCNN) to solve the above issue.
A Survey of Deep Reinforcement Learning in Video Games
In this paper, we survey the progress of DRL methods, including value-based, policy gradient, and model-based algorithms, and compare their main techniques and properties.
Non-rigid 3D shape retrieval based on multi-view metric learning
The different intrinsic representations (features) focus on different geometric properties to describe the same 3D shape, which makes the representations are related.
Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detection
Remarkably, we obtain the frame-level AUC score of 82. 12% on UCF-Crime.
Anomaly Detection In Surveillance Videos
Multiple Instance Learning
+3
BLP -- Boundary Likelihood Pinpointing Networks for Accurate Temporal Action Localization
Despite tremendous progress achieved in temporal action detection, state-of-the-art methods still suffer from the sharp performance deterioration when localizing the starting and ending temporal action boundaries.
Step-by-step Erasion, One-by-one Collection: A Weakly Supervised Temporal Action Detector
Weakly supervised temporal action detection is a Herculean task in understanding untrimmed videos, since no supervisory signal except the video-level category label is available on training data.
MRI Cross-Modality NeuroImage-to-NeuroImage Translation
Based on our proposed framework, we first propose a method for cross-modality registration by fusing the deformation fields to adopt the cross-modality information from translated modalities.
A Self-Adaptive Proposal Model for Temporal Action Detection based on Reinforcement Learning
Existing action detection algorithms usually generate action proposals through an extensive search over the video at multiple temporal scales, which brings about huge computational overhead and deviates from the human perception procedure.
Searching Action Proposals via Spatial Actionness Estimation and Temporal Path Inference and Tracking
In this paper, we address the problem of searching action proposals in unconstrained video clips.
